By interacting with radiations and by modifying the cloud reflectivity and microphysics, aerosols have important impacts on the Earth's radiative budget and water cycle (IPCC, 2013). These atmospheric particles absorb and scatter the sunlight, resulting in the so-called direct radiative effect (DRE). Although aerosols always produce a cooling effect at the Earth's surface, the sign and the amplitude of the DRE of aerosols at the top of the atmosphere depend not only on the aerosol properties but also on the reflective properties of underlying surface. For instance, in cases where absorbing aerosol layers are located above clouds, the DRE of aerosols is predominantly positive as a result of the reduction of the local planetary albedo (Keil and Haywood, 2003). By absorbing sunlight, aerosols also warm the layer of the atmosphere where they reside. This modifies the vertical profile of temperature in the atmosphere, which may affect the process of evaporation and cloud formation. This effect is called the semi-direct effect (Hansen et al., 1997; Ramanathan et al., 2001). Aerosols also impact the cloud properties by acting as cloud condensation nuclei and ice nuclei. They may modify the cloud microphysics and cloud brightness with potential impacts on precipitation and cloud lifetime (Rosenfeld, 2000; Twomey, 1974). These effects are referred as aerosol indirect effects and tend to cool the Earth.

The lack of knowledge of aerosol properties in cases of scenes of aerosols above clouds (AAC) has been recently highlighted as a source of uncertainty for the estimation of all-sky DRE of aerosols (Peers et al., 2016). Different approaches have been developed to quantify the DRE of AAC using satellite observations (Chand et al., 2009; Feng and Christopher, 2015; Meyer et al., 2013). But despite recent observational and modelling studies (De Graaf et al., 2014; Peers et al., 2015, 2016; Zhang et al., 2016), the aerosol DRE for AAC remains a subject of large uncertainty. In the process of quantification and interpretation of the aerosol impact on climate, the aerosol interactions with clouds constitute the largest uncertainty in global climate models (Myhre et al., 2013a, b). The study of AAC may also contribute to reduce those uncertainties. For instance, in case of absorbing AAC, the warming of the atmosphere occurring above stratocumulus clouds might reduce the strength of the convection and consequently impact the vertical development and the cloud properties. This warming might inhibit the entrainment of dry air at the top of the cloud, preserving the humidity of the cloud and increasing the liquid water content and the persistence of clouds (Johnson et al., 2004; Wilcox, 2010). Evidence of the first indirect effect was also found over the South Atlantic region, where AAC events are frequently observed. Costantino and Bréon (2013) notably found a strong decrease in the droplet effective radius when the aerosol layers are in contact with the top altitude of the cloud deck.

The scientific community is working on better monitoring the load and microphysical properties of AAC in order to assess the influence of those particles on the Earth's radiative budget and clouds. The constellation of satellites called the A-Train provides different passive and active sensors for monitoring clouds and aerosols (http://atrain.nasa.gov/publications/A-TrainFactSheet.pdf). Passive imagers offer larger spatial coverage but have no direct information of the vertical distribution of particles in the atmosphere. Active methods offer unique capabilities, complementary to the passive methods, and are dedicated to the study of the vertical profiles of clouds and aerosols. The main retrieved optical properties for aerosols, in “clear-sky” conditions, are the aerosol optical thickness (AOT) and the Ångström exponent (AE), which is a parameter indicative of the particles size (Kaufman et al., 2002). Recent methods also allow retrieving the aerosol single scattering albedo (SSA) over clear-sky ocean scenes (Torres et al., 2013; Waquet et al., 2016).

The active sensor Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) installed on CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite provides high-resolution vertical profiles of aerosols and clouds (Chand et al., 2008; Winker et al., 2010). CALIOP provides the total attenuated backscatter signal (km-1 sr-1) at 532 and 1064 nm. From the backscatter measurements, an operational aerosol algorithm allows for retrieval of the vertical extinction profiles as well as the AOT in clear and cloudy skies by assuming an aerosol lidar ratio (extinction to backscatter) (Omar et al., 2009; Young and Vaughan, 2009). Moreover, two orthogonally polarized channels measure the parallel and perpendicular backscatter signal at 532 nm that allows calculating the depolarization ratio (i.e. the ratio of the two orthogonal polarization signals) (Hunt et al., 2009). Depolarization measurements are used for discrimination between spherical and non-spherical particles (Sassen, 1991). CALIOP provides exhaustive details on the vertical distribution of optical and microphysical properties of aerosols and clouds, including their shape, and a qualitative classification of aerosol type (via the wavelength dependence of the backscatter) (Winker et al., 2009; Young and Vaughan, 2009).

Alternative CALIOP-based research methods have also been introduced to retrieve above-cloud AOT (ACAOT). The depolarization ratio method (DRM) (Hu et al., 2007a) and the colour ratio method (CRM) (Chand et al., 2008) use fewer assumptions for the retrieval of aerosol properties. These methods are based on light transmission methods and treat the liquid water clouds situated underneath the aerosol layer as a target. Hu et al. (2007b) have shown that, in the case of opaque water clouds, the layer-integrated attenuated backscatter at 532 nm and layer-integrated attenuated depolarization ratio at 532 nm can be used to retrieve the aerosol optical depth of the overlaying aerosol or optically thin cloud layers. The CRM uses the layer-integrated attenuated colour ratio, which is the ratio of integrated attenuated backscatter at 1064 to 532 nm. Over the visible to near-infrared spectral region, fine-mode absorbing aerosols above clouds exhibit a strong wavelength dependence colour ratio (Chand et al., 2008). This makes possible the detection of absorbing biomass-burning aerosols transported above clouds. The colour ratio observed in the case of coarse-mode particles or purely scattering fine-mode aerosols transported above clouds exhibits little or no wavelength dependence and, thus, these particles can be less accurately detected with the CRM method.

Passive sensors have also been used to obtain information on aerosols above clouds. For example, Torres et al. (2012) have developed an algorithm to retrieve the ACAOT and the underlying aerosol-corrected cloud optical depth, using radiance measurements performed in the ultraviolet (UV) by the Ozone Monitoring Instrument (OMI). The method takes advantage of the ability of biomass-burning and mineral dust aerosols to strongly absorb UV radiations. Another method that can retrieve the ACAOT and, simultaneously, the aerosol-corrected cloud optical thickness (COT) is the “colour ratio” method proposed by Jethva et al.  (2013) that employs measurements in visible and shortwave infrared channels from the Moderate Resolution Imaging Spectroradiometer (MODIS). Also, Meyer et al. (2015) developed an algorithm that employs reflectance measurements from six MODIS channels (from the visible to the shortwave infrared) to retrieve the ACAOT, as well as the COT and droplet effective radius (reff) of the underlying cloud.

The multi-directional polarization measurements have shown sensitivity to AAC scenes (Waquet et al., 2009; Hasekamp, 2010; Knobelspiesse et al., 2011). The Polarization and Directionality of Earth Reflectances (POLDER) instrument is measuring the directionality and polarization of light reflected by the Earth–atmosphere system. The aerosols generate an additional polarized light at forward and side scattering angles (70–130∘) and reduce the polarized signal of the cloud bow (i.e. a strong polarized rainbow feature observed near 140∘ in scattering angle). Mineral dust particles do not much polarize light, but they strongly minimize the cloud bow magnitude. Based on these effects, Waquet et al. (2009) have developed a method for retrieving the properties of aerosols above clouds that relies on the polarized radiances measured by POLDER. Because polarized radiances are not affected by the optical thickness of the cloud (i.e. the polarized radiance reflected by the cloud is saturated when the cloud is optically thick enough), the method is able to retrieve the scattering ACAOT at two wavelengths (670 and 865 nm) without much assumption about cloud properties. An analysis of the global results obtained with the operational algorithm is given in Waquet et al. (2013a). Furthermore, Peers et al. (2015) have developed a complementary method that uses additional total multidirectional radiances measured by POLDER. The method provides the aerosol SSA and the aerosol-corrected COT. So far, the algorithm of Peers et al. (2015) is a research method, only applied for regional studies (Peers et al., 2016).

Jethva et al. (2014) performed an intercomparative analysis of the ACAOT retrieved with the aforementioned methods in order to assess the consistency (or lack thereof) between the two independently derived ACAOTs. The results were encouraging and, despite the use of different assumptions and measurements, a close agreement was reported over homogeneous clouds. Similar to this study, our paper will focus on the comparison between collocated active and passive AAC inversion products, improving our understanding of the ACAOT. But, compared to Jethva et al. (2014), who focused only on two study cases, we perform a global and multi-annual investigation to provide robust statistics results. The vertical distribution of the aerosol and cloud layer will be also considered. We will concentrate on the following methods: (a) the CALIOP operational method (CALIOPOM) because of the numerous studies in which it was used, (b) the DRM developed by Hu et al. (2007a), (c) a calibrated version of the DRM algorithm and (d) the POLDER polarization method. The DRM and POLDER methods were chosen because both are measuring AAC properties above the same type of cloudy scenes (i.e. optically thick and homogeneous liquid water clouds). Moreover, both techniques are sensitive to all types of particles (scattering or absorbing particles, fine or coarse ones), which is not the case for CRM, which can operate only for absorbing aerosols. It is also interesting to compare these two approaches since the POLDER method requires a hypothesized aerosol microphysics, while DRM does not require any hypothesis for the aerosols but does require assumptions and an approximate model to estimate the signal backscattered by clouds.

To begin with, we briefly recall the principle of each algorithm and the data selection strategy. The results of AOT intercomparison are presented in Sects. 3 and 4. We first present a regional comparison and then describe a global comparison for a period of 4.5 years in function of the type of aerosols and AAC scenes (aerosol and cloud layers in contact or well separated). Discussions and conclusions will be drawn in Sects. 5 and 6.

The results presented in this section were acquired from May to October 2008. We selected three distinctive regions (see Fig. 2) that are under the influence of various aerosol species and different types of clouds: (a) an area that extends from 30∘ S to 5∘ N and 12∘ W to 14∘ E over the South Atlantic Ocean (SAO), (b) an area between 10 and 35∘ N and 10 and 40∘ W over the North Atlantic Ocean (NAO) and (c) an area located between 35 and 60∘ N and 140 and 170∘ E over the North Pacific Ocean (NPO). The south of the African continent is the main contributor to biomass-burning aerosols above clouds, originating from manmade crops fires (Waquet et al., 2013b). These aerosols are highly absorbing (SSA of approximately 0.84 at 865 nm) and associated with high AE values; they mainly contribute to the fine mode. The NAO area is mainly under the influence of dust aerosols originating from the Sahara for the time period of interest. These particles are mainly non-spherical and contribute primary to the coarse mode. They are moderately absorbing at the wavelength of CALIOP (532 nm) and almost non-absorbing at 865 nm (SSA of approximately 0.98) (Balkanski et al., 2007; Dubovik et al., 2002; Peers et al., 2015). The NPO is associated with various types of particles: fine-mode aerosols with rather scattering properties originating from manmade pollution (Waquet et al., 2013a; Yu et al., 2008), biomass burning from forest fires (Peers et al., 2015; Zhang et al., 2003) and dust originated from the Asian deserts. Potential mixture of these different species is also possible for this area (Yu et al., 2006).

Cloud types and their associated optical and microphysical properties are expected to be different in these three regions (Warren et al., 1988). Low-level stratocumulus clouds typically cover the SAO, with some occurrences of cumulus and altostratus clouds. Cumulus, altostratus clouds and some stratocumulus clouds generally cover the NAO. The cloud cover is generally fractional over this part of the Atlantic Ocean. Stratocumulus clouds also frequently cover the NPO. Higher altostratus and cumulus clouds are also often observed over this area. Cirrus clouds can be frequently found at mid-latitudes and also in the intertropical convergence zone, which includes the NPO and the NAO regions.

We studied 6 months of data over each region to observe the consistency between different techniques for various types of aerosols. For this part of the study, we mixed the two-layer and multiple-layer situations and we analysed all the data, disregarding the position of the aerosol and cloud layers. A case study was selected for each region in order to show the spatial variability of the AOT at 532 nm retrieved along the CALIOP transect. The first case is related to a biomass-burning event detected off the coast of Namibia on 13 August 2006. The second event concerns Saharan dust lifted above clouds westwards over the NAO on 4 August 2008, and the third case concerns Siberian biomass-burning aerosols transported over the Okhotsk Sea on 3 July 2008.

Figure 1 presents the backscatter profile at 532 nm and at 1064 nm (km-1 sr-1) of the lidar CALIOP for the three case studies, which directly provides information on the aerosol and cloud vertical distribution. In addition, the AOT and AE values measured by different techniques are presented along the CALIOP track. Additional results for the study cases comparison are shown in Table 1.

Figure 3 shows the regional comparison between the AOT and AE retrieved with POLDER and DRMSODA for a period of 6 months in 2008. The retrieval of aerosol type becomes difficult at small AOT. Therefore the AE comparison was performed only when the values of POLDER AOT at 865 nm and DRMSODA AOT at 532 nm were larger than 0.1. The AE mean value is shown with a dashed blue line. The lateral histograms show the data distribution. For the AOT comparison the colour scale represents the POLDER AE670/865. In the case of AE comparison, the POLDER AOT532nm was also reported with a colour scale. The above-mentioned description is also considered in Fig. 4, which presents the regional comparison between the AOT and AE retrieved with POLDER and CALIOPOM for the same period. Additional results for the regional intercomparison are reported in Table 2.

In the first part of this section, we quantify and discuss the overall differences found between the active and passive methods in terms of the retrieved AOT. In the second part, we address more specifically the attached cases and make hypotheses regarding the meaning of these results.

On average, the difference between POLDER and DRMSODA AOTs at 532 nm is equal to 0.073 for the detached cases and 0.087 for the undetermined cases. These differences slightly decrease to 0.065 and 0.068, respectively, when we account for the dependency of the cloud droplets lidar ratio (Sc) to reff in Eq. (10). The POLDER AOTs are systematically smaller than the ones retrieved with DRMHu. On average, these differences between these two methods are equal to -0.039 and -0.057, for the detached cases, and reach -0.036 and -0.048 for the undetermined cases, respectively without and with corrections for Sc. Thereby, the POLDER AOT estimates range, on average, between the DRMHu and DRMSODA ones. The differences in AOTs found between the POLDER method and the two DRM ones could be set to zero by modifying the lidar calibration by roughly ±10 %. One another main difference between the three methods is their different responses in terms of AOT when the atmosphere above the clouds becomes pristine. The majority of AOT (94 %) is lower than 0.1 at 865 nm for the undetermined cases. For these cases, the POLDER algorithm retrieves a mean AOT of about 0.04 at 865 nm. The accuracy of the POLDER AOT product is on the same order of magnitude. For an AOT865nm of 0.2, the error for a real refractive index uncertainty of ±0.06 would be about 0.05; for an imaginary refractive index uncertainty of ±0.01, the error would be of 0.02 (Peers et al., 2015). The impact of the assumed refractive index is lower at smaller AOT (especially for an AOT of 0.04). The background of the extrapolated POLDER AOT at 532 nm for the undetermined cases reaches 0.09. This latter value is only reported for the sake of comparison with the two other methods since the Ångström exponent retrieved by POLDER (and consequently the AOT extrapolated at 532 nm) cannot be accurately retrieved for low AOTs. DRMSODA found a mean AOT of about 0.005 at 532 nm for the undetermined cases (see Fig. 6c). The result is likely due to the re-calibration process since DRMHu found a background even larger than that of POLDER: about 0.12 at 532 nm. It is difficult to assess the truthfulness of this background, considering the given level of accuracy of the POLDER method and the uncertainties associated with the lidar calibration. We assume that these background values are not physical and could be due to some inherent limitations of the retrieval methods. From our data, however, we cannot exclude the possibility that there is always a background loading of particles above clouds (e.g. aerosols or fine droplets in formation). Nevertheless, the main result of our investigation is that POLDER and DRM methods compare well for most situations with a mean difference of about ±0.07 in AOT at 532 nm.

Although the number of cases is small, the results of the attached cases are interesting. They suggest that the lidar CALIOP and POLDER could be affected by layers of aerosols that physically and locally interact with the upper part of the cloud. In order to understand how the vertical profiles differ from one situation to another, we compared the CALIOP attenuated backscatter coefficient for attached and detached cases. We considered the period 2006–2010 and used data acquired over the entire globe. We only select the attached and detached cases where the cloud top altitude is below 1.5 km, the COT is larger than 5 and the DRMSODA AOT532nm is larger than 0.1. These criteria allow for selection of data that correspond to AAC events associated with similar cloud vertical extents and with significant AOTs. For these cases, we computed the average and median of the CALIOP level 1 attenuated backscatter coefficients at 532 nm. Figure 10 presents these results and some information concerning the mean and median values of CALIOP level 2 products: cloud top altitude, aerosol layer's base and top altitudes. The mean and median values computed for the AOTs retrieved by POLDER and DRMSODA and the numbers of sampled events are also reported. Two different types of profiles can be observed for the detached and attached situations. For the detached cases, the aerosol and cloud backscattering profiles can be easily distinguished in both the median and mean profiles. The strong peaks in the backscatter profiles at around 1 km correspond to the top of the clouds, whereas the increase in the lidar backscatter signal observed between 2 and 4 km in altitude comes from the aerosols. For the attached situations, the backscatter profiles are noisier, which is likely due to the fact that the number of detected events is smaller compared to detached cases. The top of the cloud layer is still clearly visible in the mean and median backscattered lidar signals, but two maxima can be observed. We assume that we sampled two different regimes of clouds. In addition, there is a continuous transition in the backscatter signal between the top of the cloud and the above molecular atmosphere that is most clearly visible in the median profiles. This signal does not appear for the detached cases. This signal could explain the non-negligible above-cloud AOTs retrieved by POLDER and DRMSODA for the attached cases (see Fig. 5). It is difficult to assess the origin of the signal. It might be due to aerosols layers that penetrate the cloud layers at the top of the clouds. Natural aerosol or fine droplets in formation, commonly present in the vicinity of the clouds, might also create this additional signal.

Another hypothesis that could explain the low AOT correlation for the attached cases is that the aerosols located within the cloud layer could affect the polarized radiances measured by POLDER. Note that the polarized radiance at 865 nm is not affected by the vertical position of the aerosol layer as long as there is no contact between the aerosol and the cloud. Since the operational algorithm developed for POLDER assumes that the entire aerosol layer is located above the clouds, an additional polarized signal coming from aerosol located within the cloud would lead to an overestimation of the above-cloud AOT retrieved from POLDER. To test this assumption, we modelled the polarized radiance measured by POLDER for AAC scenes, considering different vertical locations of the aerosol layer (Fig. 11). We used the SOS radiative transfer code (Lenoble et al., 2007) for this simulation. We considered a liquid water cloud located between 0 and 1 km. The particles (aerosol and cloud) are vertically homogeneously mixed. The COT is equal to 10 and the effective radius and variance are equal to 10 µm and 0.08, respectively. The aerosol layer is characterized by an AOT of 0.25 at 865 nm, a refractive index of m=1.47-0.01i and an effective radius of 0.15 µm. Figure 11 shows the typical polarized feature for AAC events in case of detached situations (i.e. aerosols located between 1.25 and 1.75 km): a creation of polarization is observed at side and forward scattering angles, whereas the cloud bow magnitude decreases. For the attached case (aerosols between 0.75 and 1.25 km), the amount of polarization created at forward scattering angles decreases and the cloud bow attenuation is less significant in comparison with the detached scenario. When the aerosol layer is located within the upper part of the cloud layer (between 0.5 and 1 km) we still observe a weak polarized signal created at forward scattering angles. When the aerosol layer is located in the lower part of the cloud layer, the effects of the aerosols disappear since the polarized radiance scattered by the aerosols is lost due to multiple scattering occurring within the clouds. These simulations were processed with the POLDER algorithm (Waquet et al., 2013b). We recall that the LUTs used in this algorithm were built for detached situations. The algorithm retrieved an AOT of 0.09 at 865 nm when the aerosols are located within the upper part of the cloud layer. This demonstrates that polarized radiances are sensitive to aerosols situated within the clouds for the attached cases.

The DRM methods might also be impacted by the presence of aerosols within the clouds. Aerosols as a solution within the cloud droplets (i.e. internal mixture) might impact the chemical composition of the droplets and modify their ability to backscatter light. Figure 9 shows lidar ratio computed for absorbing cloud droplets. We used an imaginary part of 0.0001 for the complex refractive index of the droplets. This might simulate, for instance, the properties of brown clouds contaminated by absorbing aerosols. The chosen value is in agreement with the refractive indices given for water containing soot inclusions with volume fractions ranging between 10-4 and 10-2 (Erlick, 2006). We observe a drastic increase of Sc with reff (from 21.7 sr at 5 µm to 50 sr at 40 µm) when the water droplet is weakly absorbing. In the case of an external mixture, we assume that the presence of aerosols at the top of cloud might also modify the value of Sc. Any deviation from the 19 sr value assumed for the droplets lidar ratio in Eq. (9) will necessarily impact the retrieved AOT and the differences observed between the AOT estimates provided by the POLDER and DRM methods.

In this study, we compared and analysed the consistency of the AOT and AE retrievals above clouds from different passive and active techniques. We used the CALIOP operational algorithm (Winker et al., 2009), the POLDER polarization method (Waquet et al., 2013b) and the CALIOP-based depolarization ratio method (DRMHu) (Hu et al., 2007a), for which we proposed a re-calibrated version of the DRM algorithm (DRMSODA). The observations were made for (a) three case studies corresponding to an African biomass-burning event, a Saharan dust event and a Siberian biomass-burning event; (b) a regional-scale analysis over South Atlantic Ocean, North Atlantic Ocean and North Pacific Ocean for a period of 6 months in 2008; and (c) a global-scale analysis for different vertical layer distributions for the period 2006–2010.

In the regional analysis, we observed that POLDER method and DRM are in good agreement when the microphysics of aerosols is dominated by fine-mode particles of biomass-burning aerosols (in the South Atlantic region, R2= 0.83) or coarse-mode aerosols of dust (in the North Atlantic region, R2= 0.82). A good correlation between these methods (R2= 0.68) is also noticed in the global treatment, when the aerosol and cloud layers are well separated. Nevertheless, some of the detached cases considered in our study, mainly the ones associated with optically thick smoke layers, are likely to be incorrectly classified as detached. As a future perspective, these misclassified detached cases (due to strong attenuation of the CALIOP 532 nm signal) could be detected by controlling the CALIOP 1064 nm signal, which was shown to provide more sensitivity to the entire vertical extent of these absorbing aerosol layers. The CALIOP operational method largely underestimates the AOT above clouds in all situations, with respect to other methods.

The differences between the DRM and POLDER retrievals increase when a complex mixture of aerosols is expected (such as in the East Asia region). This is probably due to the fact that the current algorithm developed for POLDER uses a limited number of microphysical models of aerosols. Also, the relative position of the aerosol layer above the cloud impacts the AOT retrievals from both active and passive measurements: the correlation decreases when the layers are in contact (R2= 0.36), suggesting that aerosols at the top or within the cloud can affect the AOT retrievals. One hypothesis is that an additional polarized signal coming from aerosol located within the cloud could affect the polarization signal and method, which leads to an overestimation of the AOT retrieved with POLDER algorithm. The aerosols attached with or within the cloud also have the potential to impact the DRM retrievals by modifying the lidar ratio (and consequently the AOT) as a result of internal or external mixture.

Furthermore, we investigated potential biases in the retrieved AOT measured by POLDER and DRMSODA as a function of MODIS cloud properties (i.e. droplet effective radius (reff) and COT). The tendencies show an increase in the difference between the two methods for larger reff, suggesting sensitivity to the cloud droplet effective radius. For this reason, we recalculated the DRMSODA AOT532nm, taking into account the dependence of lidar ratio on reff, as this method assumes a constant lidar ratio regarding the droplet effective radius. By doing so, we observed a decrease in the difference between POLDER and DRM methods and a shift of the DRM AOT values from negative to positive. For a better accuracy of DRM retrievals in future studies, this correction should be taken into account. The results show also that the multiple scattering processes, which are more pronounced in optically thick clouds, could also affect the DRM technique.

All of the aforementioned situations have revealed that DRMHu has larger mean AOT than that of DRMSODA. This is likely to be a consequence of the re-calibration performed for the DRMSODA method. Actually, POLDER AOT532nm values are consistently smaller than the ones of DRMHu and larger than those of DRMSODA. The primary conclusion of our investigation is that POLDER and DRM techniques are comparable for the majority of cases, with a mean difference of about ±0.07 in AOT at 532 nm, depending on lidar calibration.

Given the fact that each method relies upon different physical concepts, applied to different sensors and measurements, the high value of the correlation obtained for the AOT retrievals is a remarkable result that highlights the coherence between active and passive methods for aerosols above clouds. Nonetheless, more efforts have to be done to increase the accuracy of the methods in order to better understand aerosols above clouds and their related effects. Airborne measurements are extremely useful in providing information on aerosols' above-cloud properties. Several ongoing and planned airborne field campaigns will attempt to characterize the properties of biomass-burning aerosols over the South Atlantic Ocean (Zuidema et al., 2016). Planned measurements from the French Falcon 20 aircraft, equipped with a high-resolution lidar, an airborne sun photometer and a POLDER-like sensor, will notably be considered for a future validation of CALIOP DRM and POLDER above-cloud aerosol products. Another perspective is to improve the POLDER algorithm by introducing additional dust or mixture models with larger or smaller effective radii values in the LUT. This would definitely improve the AOT and AE retrievals in more complex situations (such as East Asia region). Also, our results suggest that a combination of POLDER and DRM methods has the potential to detect aerosols within clouds. It is very relevant to study these situations, since they can affect the retrievals and provide important information regarding the cloud processes. A further perspective would be to exploit the synergy between CALIOP and POLDER to infer the direct aerosol radiative forcing, aerosol heating rates and the semi-direct effect of absorbing aerosols located above clouds.